Search Results (552)

The growing demand for agile and reliable Unmanned Aerial Vehicles (UAVs) has spurred the advancement of advanced control strategies capable of ensuring stability and precision under nonlinear and uncertain flight conditions. This work addresses the challenge of accurately tracking UAV position by proposing a neural-network-based approach designed to replicate the behavior of classical control systems. A complete nonlinear model of the quadcopter was derived and linearized around a hovering point to design a traditional proportional derivative (PD) controller, which served as a baseline for training a nonlinear autoregressive exogenous (NARX) artificial neural network. The NARX model, selected for its feedback structure and ability to capture temporal dynamics, was trained to emulate the control signals of the PD controller under varied reference trajectories, including step, sinusoidal, and triangular inputs. The trained networks demonstrated performance comparable to the PD controller, particularly in the vertical axis, where the NARX model achieved a minimal Mean Squared Error (MSE) of $7.78\times {10}^{-5}$ and an $R^2$ value of 0.9852. These results confirm that the NARX neural network, trained via supervised learning to emulate a PD controller, can replicate and even improve classical control strategies in nonlinear scenarios, thereby enhancing robustness against dynamic changes and modeling uncertainties. This research contributes a scalable approach for integrating neural models into UAV control systems, offering a promising path toward adaptive and autonomous flight control architectures that maintain stability and accuracy in complex environments. Full article

To address the local optimality in path planning for logistics robots using APF (artificial potential field) and the stagnation problem when encountering trap obstacles, this paper proposes VDPF (variable-direction potential field) combined with RL (reinforcement learning) to effectively solve these problems. First, based on obstacle distribution, an obstacle classification algorithm is designed, enabling the robot to select appropriate obstacle avoidance strategies according to obstacle types. Second, the attractive force and repulsive force in APF are separated, and the direction of the repulsive force is modified to break the local optimum, allowing the robot to focus on handling current obstacle avoidance tasks. Finally, the improved APF is integrated with the TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithm, and a weight factor is introduced to adjust the robot’s acting forces. By sacrificing a certain level of safety for a larger exploration space, the robot is guided to escape from local optima and trap regions. Experimental results show that the improved algorithm effectively mitigates the trajectory oscillation of the robot and can efficiently solve the problems of local optimum and trap obstacles in the APF method. Compared with the algorithm APF-TD3 in scenarios with five obstacles, the proposed algorithm reduces the GS (Global Safety) by 8.6% and shortens the length by 8.3%. In 10 obstacle scenarios, the proposed algorithm reduces the GS by 29.8% and shortens the length by 9.7%. Full article

In many applications, including underwater robotics, the coverage problem requires an autonomous vehicle to systematically explore a defined area while minimizing redundancy and avoiding obstacles. This paper investigates coverage path-planning strategies to enhance the efficiency of underwater gliders particularly in maximizing the probability of detecting a radioactive source while ensuring safe navigation. We evaluate three path-planning approaches: the Traveling Salesman Problem (TSP), Minimum Spanning Tree (MST), and the Optimal Control Problem (OCP). Simulations were conducted in MATLAB R2020a, comparing processing time, uncovered areas, path length, and traversal time. Results indicate that the OCP is preferable when traversal time is constrained, although it incurs significantly higher computational costs. Conversely, MST-based approaches provide faster but fewer optimal solutions. These findings offer insights into selecting appropriate algorithms based on mission priorities, balancing efficiency and computational feasbility. Full article

To address the obstacle-avoidance path-planning requirements of dual-arm robots operating in complex environments, such as chemical laboratories and biomedical workstations, this paper proposes ODSN-RRT (optimization-direction-step-node RRT), an efficient planner based on rapidly-exploring random trees (RRT). ODSN-RRT integrates three key optimization strategies. First, a two-stage sampling-direction strategy employs goal-directed growth until collision, followed by hybrid random-goal expansion. Second, a dynamic safety step-size strategy adapts each extension based on obstacle size and approach angle, enhancing collision detection reliability and search efficiency. Third, an expansion-node optimization strategy generates multiple candidates, selects the best by Euclidean distance to the goal, and employs backtracking to escape local minima, improving path quality while retaining probabilistic completeness. For collision checking in the dual-arm workspace (self and environment), a cylindrical-spherical bounding-volume model simplifies queries to line-line and line-sphere distance calculations, significantly lowering computational overhead. Redundant waypoints are pruned using adaptive segmental interpolation for smoother trajectories. Finally, a master-slave planning scheme decomposes the 14-DOF problem into two 7-DOF sub-problems. Simulations and experiments demonstrate that ODSN-RRT rapidly generates collision-free, high-quality trajectories, confirming its effectiveness and practical applicability. Full article

Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. To address the issue of inadequate adaptability in current path planning strategies for dual-arm systems, this paper proposes a novel path planning method for dual-arm picking (LTSACO). The technique centers on a dynamic-weight heuristic strategy and achieves optimization through the following steps: first, the K-means clustering algorithm divides the target area; second, the heuristic mechanism of the Ant Colony Optimization (ACO) algorithm is improved by dynamically adjusting the weight factor of the state transition probability, thereby enhancing the diversity of path selection; third, a 2-OPT local search strategy eliminates path crossings through neighborhood search; finally, a cubic Bézier curve heuristically smooths and optimizes the picking trajectory, ensuring the continuity of the trajectory’s curvature. Experimental results show that the length of the parallelogram trajectory, after smoothing with the Bézier curve, is reduced by 20.52% compared to the gantry trajectory. In terms of average picking time, the LTSACO algorithm reduces the time by 2.00%, 2.60%, and 5.60% compared to DCACO, IACO, and the traditional ACO algorithm, respectively. In conclusion, the LTSACO algorithm demonstrates high efficiency and strong robustness, providing an effective optimization solution for multi-arm cooperative picking and significantly contributing to the advancement of multi-arm robotic picking systems. Full article

The Adaptive Cross Ivy (ACIVY) algorithm is a novel bio-inspired metaheuristic that emulates ivy plant growth behaviors for complex optimization problems. While the original Ivy Optimization Algorithm (IVYA) demonstrates a competitive performance, it suffers from limited inter-individual information exchange, inadequate directional guidance for local optima escape, and abrupt exploration–exploitation transitions. To address these limitations, ACIVY integrates three strategic enhancements: the crisscross strategy, enabling horizontal and vertical crossover operations for improved population diversity; the LightTrack strategy, incorporating positional memory and repulsion mechanisms for effective local optima escape; and the Top-Guided Adaptive Mutation strategy, implementing ranking-based mutation with dynamic selection pools for smooth exploration–exploitation balance. Comprehensive evaluations on the CEC2017 and CEC2022 benchmark suites demonstrate ACIVY’s superior performance against state-of-the-art algorithms across unimodal, multimodal, hybrid, and composite functions. ACIVY achieved outstanding average rankings of 1.25 (CEC2022) and 1.41 (CEC2017 50D), with statistical significance confirmed through Wilcoxon tests. Practical applications in engineering design optimization and UAV path planning further validate ACIVY’s robust performance, consistently delivering optimal solutions across diverse real-world scenarios. The algorithm’s exceptional convergence precision, solution reliability, and computational efficiency establish it as a powerful tool for challenging optimization problems requiring both accuracy and consistency. Full article

The escalating consumption of fossil fuels and the rapid development of portable electronics have increased interest in alternative energy solutions that can sustainably self-power wearable devices. Triboelectric nanogenerators (TENGs), which convert mechanical energy into electricity through contact electrification and electrostatic induction, have emerged as a promising technology due to their high voltage output, lightweight design, and simple fabrication. However, the performance of TENGs is often limited by a low surface charge density, inadequate dielectric properties, and poor charge retention of triboelectric materials. To address these challenges, recent research has focused on the use of polymer composites that incorporate various functional fillers. The filler materials play roles in improving dielectric performance and enhancing mechanical durability, thereby boosting triboelectric output even in harsh environments, while also diminishing charge loss. This review comprehensively examines the role of polymer composite design in TENG performance, with particular emphasis on materials categorized by their triboelectric polarity. Tribo-negative polymers, such as PDMS and PVDF, benefit from filler incorporation and phase engineering to enhance surface charge density and charge retention. By contrast, tribo-positive materials like nylon and cellulose have demonstrated notable improvements in mechanical robustness and environmental stability through composite strategies. The interplay between material selection, surface engineering, and filler design is highlighted as a critical path toward developing high-performance, self-powered TENG systems. Finally, this review discusses the current challenges and future opportunities for advancing TENG technology toward practical and scalable applications. Full article

The efficient depolymerization of lignin has become a key challenge in the preparation of high-value-added chemicals. Graphitic carbon nitride (g-C₃N₄)-based photocatalytic system shows potential due to its mild and green characteristics over other depolymerization methods. However, its inherent defects, such as a wide band gap and rapid carrier recombination, severely limit its catalytic performance. In this paper, a g-C₃N₄ modification strategy of K⁺ doping and surface alkalinization is proposed, which is firstly applied to the photocatalytic depolymerization of the lignin β-O-4 model compound (2-phenoxy-1-phenylethanol). K⁺ doping is achieved by introducing KCl in the precursor thermal polymerization stage to weaken the edge structure strength of g-C₃N₄, and post-treatment with KOH solution is combined to optimize the surface basic groups. The structural/compositional evolution of the materials was analyzed by XRD, FTIR, and XPS. The morphology/element distribution was visualized by SEM-EDS, and the optoelectronic properties were evaluated by UV–vis DRS, PL, EIS, and transient photocurrent (TPC). K⁺ doping and surface alkalinization synergistically regulate the layered structure of the material, significantly increase the specific surface area, introduce nitrogen vacancies and hydroxyl functional groups, effectively narrow the band gap (optimized to 2.35 eV), and inhibit the recombination of photogenerated carriers by forming electron capture centers. Photocatalytic experiments show that the alkalinized g-C₃N₄ can completely depolymerize 2-phenoxy-1-phenylethanol with tunable product selectivity. By adjusting reaction time and catalyst dosage, the dominant product can be shifted from benzaldehyde (up to 77.28% selectivity) to benzoic acid, demonstrating precise control over oxidation degree. Mechanistic analysis shows that the surface alkaline sites synergistically optimize the C_β-O bond breakage path by enhancing substrate adsorption and promoting the generation of active oxygen species (·OH, ·O₂⁻). This study provides a new idea for the efficient photocatalytic depolymerization of lignin and lays an experimental foundation for the interface engineering and band regulation strategies of g-C₃N₄-based catalysts. Full article

Technology-based strategies for the prevention and management of suicidal behavior are widely referenced for identifying vulnerable groups and for supporting clinical reasoning, decision-making, and appropriate referrals. In this study, we estimated the interface and content validity evidence of an interactive mobile application developed for managing suicidal behavior. This methodological study employed psychometric parameters to evaluate the content and interface of the mobile application, following five action phases: analysis, design, development, implementation, and evaluation. A total of 27 healthcare professionals participated, selected by convenience sampling, all working within the Psychosocial Care Network across different regions of Brazil. Data were collected using an electronic form, the Delphi technique for evaluation rounds, and a Likert scale to achieve consensus. The validity analysis was based on a Content Validity Index (CVI) equal to or greater than 0.80. The results showed that GerenciaVida, a technology developed for healthcare workers—regardless of their level of care or professional category—can be used to screen for suicide risk in the general population and indicate preventive alternatives. The app demonstrated satisfactory indicators of content validity (0.974) and interface validity (0.963), reflecting clarity (0.925), objectivity (1.00), adequacy (0.925), coherence (0.962), accuracy (0.962), and clinical relevance (1.00). The development path of this mobile application provided scientific, technological, and operational support, establishing it as an innovative care tool. It consolidates valid evidence that supports the identification, risk classification, and prevention of suicidal behavior in various healthcare contexts. Full article

As the resolution and data volume of remote sensing imagery continue to grow, achieving efficient compression without sacrificing reconstruction quality remains a major challenge, given that traditional handcrafted codecs often fail to balance rate-distortion performance and computational complexity, while deep learning-based approaches offer superior representational capacity. However, challenges remain in achieving a balance between fine-detail adaptation and computational efficiency. Mamba, a state–space model (SSM)-based architecture, offers linear-time complexity and excels at capturing long-range dependencies in sequences. It has been adopted in remote sensing compression tasks to model long-distance dependencies between pixels. However, despite its effectiveness in global context aggregation, Mamba’s uniform bidirectional scanning is insufficient for capturing high-frequency structures such as edges and textures. Moreover, existing visual state–space (VSS) models built upon Mamba typically treat all channels equally and lack mechanisms to dynamically focus on semantically salient spatial regions. To address these issues, we present an innovative architecture for distant sensing image compression, called the Multi-scale Channel Global Mamba Network (MGMNet). MGMNet integrates a spatial–channel dynamic weighting mechanism into the Mamba architecture, enhancing global semantic modeling while selectively emphasizing informative features. It comprises two key modules. The Wavelet Transform-guided Local Structure Decoupling (WTLS) module applies multi-scale wavelet decomposition to disentangle and separately encode low- and high-frequency components, enabling efficient parallel modeling of global contours and local textures. The Channel–Global Information Modeling (CGIM) module enhances conventional VSS by introducing a dual-path attention strategy that reweights spatial and channel information, improving the modeling of long-range dependencies and edge structures. We conducted extensive evaluations on three distinct remote sensing datasets to assess the MGMNet. The results of the investigations revealed that MGMNet outperforms the current SOTA models across various performance metrics. Full article

Autonomous exploration of autonomous vehicles in off-road environments remains challenging due to the adverse impact on exploration efficiency and safety caused by uneven terrain. In this paper, we propose a path planning framework for autonomous exploration to obtain feasible and smooth paths for autonomous vehicles in 3D off-road environments. In our framework, we design a target selection strategy based on 3D terrain traversability analysis, and the traversability is evaluated by integrating vehicle dynamics with geometric indicators of the terrain. This strategy detects the frontiers within 3D environments and utilizes the traversability cost of frontiers as the pivotal weight within the clustering process, ensuring the accessibility of candidate points. Additionally, we introduced a more precise approach to evaluate navigation costs in off-road terrain. To obtain a smooth local path, we generate a cluster of local paths based on the global path and evaluate the optimal local path through the traversability and smoothness of the path. The method is validated in simulations and real-world environments based on representative off-road scenarios. The results demonstrate that our method reduces the exploration time by up to 36.52% and ensures the safety of the vehicle while exploring unknown 3D off-road terrain compared with state-of-the-art methods. Full article

Microbial infections are an escalating global health threat, driven by the alarming rise of antimicrobial resistance (AMR), which has made many conventional antibiotics increasingly ineffective and threatens to reverse decades of medical progress. The rapid emergence and spread of multidrug-resistant bacteria have severely limited treatment options, resulting in increased morbidity, mortality, and healthcare burden worldwide. In response to these challenges, phage therapy is regaining interest as a promising alternative. Bacteriophages, the most abundant biological entities, have remarkable specificity toward their bacterial hosts, enabling them to selectively eliminate pathogenic strains. Phage therapy presents several advantages over conventional antibiotics, which include minimal disruption to the microbiome and a slower rate of resistance development. Among the various sources of phages, the marine environment remains one of the least explored. Given their adaptation to saline conditions, high pressure, and variable nutrient levels, marine bacteriophages mostly exhibit enhanced environmental stability, broader host ranges, and distinct infection mechanisms, thus making them highly promising for therapeutic purposes. This review explores the growing therapeutic potential of marine bacteriophages by examining their ecological diversity, biological characteristics, infection dynamics, and practical applications in microbial disease control. It also deals with emerging strategies such as phage–antibiotic synergy, genetic engineering, and the use of phage-derived enzymes, alongside several challenges that must be addressed to enable clinical translation and regulatory approval. Advancing our understanding and application of marine phages presents a promising path in the global fight against AMR and the development of next-generation antimicrobial therapies. Full article

Path planning and collision avoidance are essential components of an autonomous driving system (ADS), ensuring safe navigation in complex environments shared with other road users. High-quality planning and reliable obstacle avoidance strategies are essential for advancing the SAE autonomy level of autonomous vehicles, which can largely reduce the risk of traffic accidents. In daily driving scenarios, lane changing is a common maneuver used to avoid unexpected obstacles such as parked vehicles or suddenly appearing pedestrians. Notably, lane-changing behavior is also widely regarded as a key evaluation criterion in driver license examinations, highlighting its practical importance in real-world driving. Motivated by this observation, this paper aims to develop an autonomous lane-changing system capable of dynamically avoiding obstacles in multi-lane traffic environments. To achieve this objective, we propose a hierarchical decision-making and control framework in which a Double Deep Q-Network (DDQN) agent operates as the high-level planner to select lane-level maneuvers, while a High-Order Control Lyapunov Function–High-Order Control Barrier Function–based Quadratic Program (HOCLF-HOCBF-QP) serves as the low-level controller to ensure safe and stable trajectory tracking under dynamic constraints. Simulation studies are used to evaluate the planning efficiency and overall collision avoidance performance of the proposed hierarchical control framework. The results demonstrate that the system is capable of autonomously executing appropriate lane-changing maneuvers to avoid multiple obstacles in complex multi-lane traffic environments. In computational cost tests, the low-level controller operates at 100 Hz with an average solve time of 0.66 ms per step, and the high-level policy operates at 5 Hz with an average solve time of 0.60 ms per step. The results demonstrate real-time capability in autonomous driving systems. Full article

With the rapid evolution of social computing, online sentiments have become valuable information for analyzing the latent structure of social networks. Sentiment leaders in social networks are those who bring in new information, ideas, and innovations, disseminate them to the masses, and thus influence the opinions and sentiment of others. Identifying sentiment leaders can help businesses predict marketing campaigns, adjust marketing strategies, maintain their partnerships, and improve their products’ reputations. However, capturing the complex sentiment dynamics from multi-hop interactions and trust/distrust relationships, as well as identifying leaders within sentiment-aligned communities while maximizing sentiment spread efficiently through both direct and indirect paths, is a significant challenge to be addressed. This paper pioneers a challenging and important problem of sentiment leader identification in social networks. To this end, we propose an original solution framework called “SentiRank” and develop the associated algorithms to identify sentiment leaders. SentiRank contains three key technical steps: (1) constructing a sentiment graph from a social network; (2) detecting sentiment communities; (3) ranking the nodes on the selected sentiment communities to identify sentiment leaders. Extensive experimental results based on the real-world datasets demonstrate that the proposed framework and algorithms outperform the existing algorithms in terms of both one-step sentiment coverage and all-path sentiment coverage. Furthermore, the proposed algorithm performs around 6.5 times better than the random approach in terms of sentiment coverage maximization. Full article

Against the backdrop of the energy crisis and a rapidly advancing digital economy, electricity–computing synergy has become a strategic pathway to resolve energy constraints in computing power center and overcome renewable energy consumption challenges. This study breaks through the existing single-factor fragmented analysis method and systematically constructs a vertically progressive and horizontally coupled electricity–computing synergy planning model to deconstruct the core elements of computing power center construction and reconstruct the path of electricity–computing value co-creation. It proposes a multi-objective site selection decision-making method for computing power center based on linear weighting and the principle of reusability and universality, effectively avoiding the problem of overall system efficiency loss caused by single-objective optimization. Based on the empirical results from data from 31 provinces in China, this study classifies the endowments for computing power center construction, conducts targeted analyses of each province’s situation, and finds three major contradictions facing China’s computing power center: a spatial mismatch between green energy resources and service demand, a dynamic imbalance between electricity price advantages and comprehensive costs, and structural contradictions between talent reserves and sustainable development. Finally, a multi-dimensional integrated strategy was systematically constructed, encompassing demand-driven initiatives, electricity price adjustments, talent innovation, natural cold-source activation, and network upgrades, to provide guidance and policy toolkits for the government planning of computing power infrastructure development. Full article